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arXiv:2105.01109 · International Conference for High Performance Computing, Networking, Storage and Analysis (SC)

Distributed quantum computing with QMPI

Thomas Häner, Damian S. Steiger, Torsten Hoefler, Matthias Troyer

Published Oct 21, 2021• 49 citations
arXivHTMLPDF

Abstract

Practical applications of quantum computers require millions of physical qubits and it will be challenging for individual quantum processors to reach such qubit numbers. It is therefore timely to investigate the resource requirements of quantum algorithms in a distributed setting, where multiple quantum processors are interconnected by a coherent network. We introduce an extension of the Message Passing Interface (MPI) to enable high-performance implementations of distributed quantum algorithms. In turn, these implementations can be used for testing, debugging, and resource estimation. In addition to a prototype implementation of quantum MPI, we present a performance model for distributed quantum computing, SENDQ. The model is inspired by the classical LogP model, making it useful to inform algorithmic decisions when programming distributed quantum computers. Specifically, we consider several optimizations of two quantum algorithms for problems in physics and chemistry, and we detail their effects on performance in the SENDQ model.

DOI
10.1145/3458817.3476172

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Practical applications of quantum computers require millions of physical qubits and it will be challenging for individual quantum processors to reach such qubit numbers. It is therefore timely to investigate the resource requirements of quantum algorithms in a distributed setting, where multiple quantum processors are interconnected by a coherent network. We introduce an extension of the Message Passing Interface (MPI) to enable high-performance implementations of distributed quantum algorithms. In turn, these implementations can be used for testing, debugging, and resource estimation. In addition to a prototype implementation of quantum MPI, we present a performance model for distributed quantum computing, SENDQ. The model is inspired by the classical LogP model, making it useful to inform algorithmic decisions when programming distributed quantum computers. Specifically, we consider several optimizations of two quantum algorithms for problems in physics and chemistry, and we detail their effects on performance in the SENDQ model.
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Quantum computers today are noisy and limited in the number of qubits that can be set with reasonable fidelities and gate times for each system. Since the current limit on the number of qubits is difficult to surpass, it is expected that the future of quantum computing will lie in connecting different quantum computers together to form a quantum network, each with its own limitations. Herein lies the field of distributed quantum computing optimized for large-scale communication. One such system that has been designed is the Quantum Message Passing Interface (QMPI). This interface, unlike the classical MPI, takes advantage of quantum teleportation to communicate between different quantum nodes but leverages the framework built for classical MPI. This general interface is essential as we try to connect various quantum computers, as it can be used to develop distributed quantum algorithms across various nodes. In this work, we develop a reference implementation for a QMPI interface in Qiskit and characterize its performance. In our interface, we have implemented point-to-point and optimized collective operations for larger distributed quantum networks. To test our framework, we demonstrate our implementation with applications that can leverage the QMPI framework including Trotterization of the transverse field Ising model and distributive quantum phase estimation. Lastly, we have developed a new technique for decomposing quantum gates across many nodes which leverages multi-controlled Toffolli gates. This technique beats the state of the art for an adder circuit in terms of fidelity and the number of EPR pairs used.
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Distributed Quantum Computing (DQC) is essential for scaling quantum algorithms beyond the limitations of monolithic NISQ devices. However, the current software ecosystem forces developers to manually orchestrate low-level network resources, such as entanglement generation (EPR pairs) and classical synchronization, leading to verbose, error-prone, and non-scalable code. This paper introduces \textbf{NetQMPI}, a high-level Python framework that adapts the Message Passing Interface (MPI) standard to the quantum domain using the Single Program Multiple Data (SPMD) paradigm. Built as a middleware over the NetQASM SDK, NetQMPI abstracts the underlying physical topology, automating network initialization and resource management through a unified Communicator interface. We propose semantic point-to-point primitives and novel collective operations--such as expose and unexpose--that address the constraints of the No-Cloning Theorem by leveraging multipartite entanglement for data distribution. Our comparative analysis demonstrates that NetQMPI decouples algorithmic logic from network size, reducing the code complexity for generating an NNN-node GHZ state from O(N2)\mathcal{O}(N^2)O(N2) to constant complexity O(1)\mathcal{O}(1)O(1). Furthermore, the framework ensures backend agnosticism, enabling the seamless execution of high-level applications on rigorous physical simulators, such as NetSquid (via SquidASM), and future quantum hardware adhering to the NetQASM standard.
Quantum computing has been an attractive method adapted for increasing the computational speed and it is governed by the laws of quantum mechanics. Quantum computers are not limited to two states as compared with classical systems, but they encode information as quantum bits or qubits. A qubit represents atoms, ions, photons or electrons and their respective control devices that are working together to act as computer memory and processors. The power of quantum computing lies in its multi-state representation which makes it million times more powerful than classical computer systems. Quantum computers also use another aspect of quantum mechanics known as entanglement, which is a property where multiple objects existing in the states that can be linked together. There exists a standard/specification of message passing library, known as message passing interface (MPI) that can be used for achieving high-performance computing. The goal of MPI is to provide a standardized framework for writing message-passing programs which can be used for programming systems that require distributed processing. The superposition principle used by the quantum algorithm can be simulated in the MPI environment with a cluster of computers or multi-CPU systems. This paper focuses on exposing the strength of quantum algorithm in high performance computing by using MPI, and also on comparing the sequential and parallel execution of programs on the basis of execution time and CPU utilization.